Mesoscale marine tropical precipitation varies independently from the spatial arrangement of its convective cells

Abstract

The relationship between mesoscale convective organization, quantified by the spatial arrangement of convection, and oceanic precipitation in the tropical belt is examined using the output of a global storm-resolving simulation. The analysis uses a 2D watershed segmentation algorithm based on local precipitation maxima to isolate individual precipitation cells and derive their properties. 10 • by 10 • scenes are analyzed using a phase-space representation made of the number of cells per scene and the mean area of the cells per scene to understand the controls on the spatial arrangement of convection and its precipitation. The presence of few and large cells in a scene indicates the presence of a more clustered distribution of cells, whereas many small cells in a scene tend to be randomly distributed. In general, the degree of clustering of a scene (I org ) is positively correlated to the mean area of the cells and negatively correlated to the number of cells. Strikingly, the degree of clustering, whether the cells are randomly distributed or closely spaced, to a first order does not matter for the precipitation amounts produced. Scenes of similar precipitation amounts appear as hyperbolae in our phase-space representation, hyperbolae that follow the contours of the precipitating area fraction. Finally, including the scene-averaged water vapour path (WVP) in our phase-space analysis reveals that scenes with larger WVP contain more cells than drier scenes, whereas the mean area of the cells only weakly varies with WVP. Dry scenes can contain both small and large cells, but they can contain only few cells of each category. K E Y W O R D Sconvection, object-based approaches, organization, precipitation, storm-resolving modelling This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cloud Patterns in the Trades Have Four Interpretable Dimensions

“…The effectiveness of our approach may well extend to descriptions of deep convective organization. Many relationships between our metrics are consistent with those found for deep convective cloud fields (Brueck et al., 2020; Rempel et al., 2017), suggesting that an effective, low‐dimensional description of deep convective organization is attainable. Our pattern description could also be used for forecast verification (Jolliffe & Stephenson, 2012), using the pattern distribution's dimensions as matching criteria between model and observation in similar fashion to, for example, the criteria developed by Wernli et al.…”

Impact of a Mixed Ocean Layer and the Diurnal Cycle on Convective Aggregation

“…We note that we could have alternatively used the classical Clark-Evans statistical test (Clark & Evans, 1954) directly on sub-sampled nearest neighbor distances of the velocity field, however, we prefer the application to Iorg as it is a scene-integrated quantity that has been widely applied in the field since its introduction (e.g. Pscheidt et al, 2019;Moseley et al, 2020;Bony et al, 2020;Brueck et al, 2020;Janssens et al, 2021).…”

A Relationship Between ITCZ Organization and Subtropical Humidity

“…Quantifying organization is not straightforward (see, e.g., Figure 1 in Brueck et al, 2020), as evidenced by the various organization indices that have been proposed recently (Brune et al, 2018; Kadoya & Masunaga, 2018; Retsch et al, 2020; Tobin et al, 2012; Tompkins & Semie, 2017; White et al, 2018). Different indices emphasize distinct aspects of organization and do not necessarily agree (Pscheidt et al, 2019; Xu et al, 2019).…”